Protein embeddings are a way to encode functional and structural properties of a protein, mostly from its sequence only, in a machine-friendly format (vector representation). Generating such …

Quickly predict protein structure and function from sequence via embeddings: embed.protein.properties. We presented the bio_embeddings pipeline as a talk at ISMB 2020 …

2024年11月7日 · Here, we present a method for integrating information from sequence and structure in a single model that we term SSEmb (Sequence Structure Embedding). SSEmb …

Multi-task and masked language model-based protein sequence embedding models. This repository contains code and links to download pre-trained models and data accompanying …

We transfer annotations from proteins with known GO terms to query sequences via embedding distance. To do so, we embed all sequences in a lookup database of proteins with known GO …

2024年10月11日 · 为了解决这一问题，Tristan Bepler和Bonnie Berger在 ICLR 2019上提出了一个创新性的方法： Learning protein sequence embeddings using information from structure。 …

2019年2月22日 · We train bidirectional long short-term memory (LSTM) models on protein sequences with a two-part feedback mechanism that incorporates information from (i) global …

2022年12月26日 · 这次为大家报道的是nature methods 上一篇题为” Deep embedding and alignment of protein sequences” 的文章，来自法国巴黎Google Research的Brain Team团队。 …

2022年12月15日 · DEDAL is a deep learning-based protein sequence alignment method that improves the quality of predicted alignment for remote homologs and better discriminates …

In this work, we address this problem by learning protein sequence embeddings using weak supervi-sion from global structural similarity for the first time. Specifically, we aim to learn a …